oracle

This repositorry contain a method to compare metagenomics and metabarcoding.

Workflow

To Do

Remaining tasks, clarifications, or questions:

• [x] make a flowchart representing our statistical analyses,
• [x] sample rarefaction: the documentation for vegan::rrarefy states that there are better ways to normalize sample sizes.

  Random rarefaction is sometimes used to remove the effects of different sample sizes. This is usually a bad idea: random rarefaction discards valid data, introduces random error and reduces the quality of the data (McMurdie & Holmes 2014). It is better to use normalizing transformations (‘decostand’ in ‘vegan’) possible with variance stabilization (‘decostand’ and ‘dispweight’ in ‘vegan’) and methods that are not sensitive to sample sizes.

Schloss, 2024 states that rarefaction is the most effective way to correct for sampling effort. He points the difference between rarefying (subsamplign one time) and rarefaction (subsampling multiple time and make the average). There are opposite views, thus we try both rarefaction and normalizing transformation.

• [ ] rarefaction curves: some curves do not reach the rarefaction threshold (vertical line). Why?
• [ ] there is a large difference (100x) in the number of assigned reads for MetaG R2 and MetaG R1. Knowing that R1 and R2 have identical number of reads at the start of the analysis, why is there much more R2 reads assigned?
• [ ] diversity indices are very different between MetaG and MetaB. Hypothesis?
• [ ] beta diversity: identify the outliers (samples ST-10, YL-19, TS-3, SER-10, more?). Are these samples the ones that were re-sequenced?
• [ ] simulate the effect of amplification on mean and variance of our species throughout many samples. The goal is to compare with our observations?
• [ ] metacoder: very large fold-change values for non-parametric tests. Why?